Among the multitude of new technologies entering banking’s sphere, machine learning likely will quickly come to the fore.

Imagine some poor bank employee sitting at a desk, peering at multiple computer screens. On those screens come waves and waves of charts, transaction lists, voice recordings, geographical data points, and social media posts, all constantly changing—and all focused on a single bank customer.

The bank employee’s job, at the moment: Decide whether that customer actually used his own credit card to buy a big screen television, or if that transaction, which just occurred, is fraudulent.

And he only has a moment to decide. Seconds later, all that particular data will be erased and replaced with waves and waves of more data associated with yet another customer and yet another transaction somewhere that might or might not be suspicious.

A tall order, indeed, yet not so far-fetched in these days of near-real-time transactions, and ultra sophisticated cyber crooks using ever-increasing technology. Making it even more intense are regulatory mandates for banks to stay on top of suspicious activity monitoring, as well as internal imperatives to mitigate potential losses, avoid false alerts, and improve customer relationships.

The standard transaction monitoring practices using traditional analytic software, backed by human oversight, may no longer be enough to meet anti-fraud work in particular.

Increasingly, the challenge will be met by banks through the use of machine learning, a subset of artificial intelligence. Through software, all those waves and waves of data can be processed much faster and with more insight than any single human could possibly do. Elements of this are already in use, particularly in regard to fraud.

Self-learning software

“We do have some software in our bank that has the ability to learn based on customer patterns and activities and transactions and use of devices and types of credit instruments, to learn about our customers’ activities, to anticipate when something looks unusual,” says Peter Graves, chief information officer, Independent Bank Co., Grand Rapids, Mich., in an interview with Banking Exchange. “That’s where you get this fraud pattern. When that pattern is broken there is a prediction that this has a high probability of being some kind of a fraud transaction because it is not what we would normally expect.” He declined to name the specific solution for competitive and security reasons.

Other bankers and analysts interviewed by Banking Exchange say much the same thing.

Sridhar Rajan, robotics and cognitive automation lead for Financial Services at Deloitte Consultants, puts it this way: “Machine learning uses technologies that can self-learn with little to no human intervention. They get better at what they need to do, the more information you feed them. The whole idea for machine learning is that those technologies don’t require constant human intervention to get better and better … They eventually mimic human judgment at high speed, high scale, and low cost.”

“Any time you have a string of transactions, you’re looking for anomalies, whether it’s credit cards or wires or account balances,” says Nichols. “If we see balances that pop way up and it’s tax time, January or February, that’s not an anomaly. But if it pops way up for a certain account in July, that’s an anomaly and our attention is called to it. That’s the purest instance about how we can turn a machine on, how it can kick out a bunch of anomalies, calls our attention to it. We can say, ʻNo, this isn’t an anomaly,’ and then it learns and so the next time that we run the program it gets smarter and doesn’t kick it out as an anomaly.”

Seeing a pattern missed by humans

Fraud isn’t the only area in which machine learning can be applied in a bank. Wells’ Stewart observes that there are possibilities that “range from systems that can spot payments fraud or misconduct by employees, to technology that can make more personal recommendations on financial products to clients.”

Nichols cites a specific example related to marketing, in which his bank conducted a project to identify its best customers—normally defined by those who have the largest balances or biggest loans.

When the bank ran its customer data base through a machine learning program, the machine picked out a group that the humans in the bank would never have found: a group of stay-at-home mothers in Florida who had a big social network, frequently posted to it, and were willing to pass along recommendations to their friends.

“We would never have known how big their networks are,” Nichols says. “So when we could do something good for the community, they would spread the word. That changed our definition of ʻbest customer’ because many of these stay-at-home moms got their friends to use CenterState.”

On the other hand, the machine learning state of the art still has a way to go in the area of credit approvals. Machine learning, says Daniel Latimore, senior vice-president, banking, at Celent, “just dives into the database and starts looking for all these correlations [that are] not necessarily causal relationships.”

Again, Nichols says his bank has experimented with applying machine learning to credit, but stepped back.

“Once the machine started to learn it started to pick up some relationships that we never thought of before. It made some decisions for us that we couldn’t figure out what it was doing. That is a problem,” he says.

Independent Bank’s Graves echoes this sentiment.

“If [the machine learning] is designed in a way that might be misleading to the customer or might take the customer down a false path, such as saying things as ʻour product’ or ʻonly solution’ you have to be careful with that from a compliance perspective,” warns Graves. “The regulations and the compliance haven’t caught up with this trend.”

Nevertheless, machine learning as well as artificial intelligence in general looks to be a very fast track.

“Right now the primary field of play is in the larger banks, because it is a heavy investment and we’re in the learning curve phase on how to do this,” says Michael Abbott, managing director of financial services/digital, North America, at Accenture. “But what I’m seeing is the fintechs are going right behind [the big banks] very rapidly, on a pace we’ve never seen before, adapting these technologies and then making them available across the banking industry.”

This is the second part of a three-part series about the application of artificial intelligence to banking.

John Ginovsky is a contributing editor of Banking Exchange and editor of the publication’s Tech Exchange e-newsletter. For more than two decades he’s written about the commercial banking industry, specializing in its technological side and how it relates to the actual business of banking. In addition to his weekly blogs—"Making Sense of It All"—he contributes fresh, original stories to each Tech Exchange issue based on personal interviews or exclusive contributed pieces. He previously was senior editor for Community Banker magazine (which merged into ABA Banking Journal) and for ABA Banking Journal and was managing editor and staff reporter for ABA’s Bankers News. Email him at [email protected]